English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Semantic segmentation is an important component in the perception systems ofautonomous vehicles. In this work, we adopt recent advances in both image andpoint cloud segmentation to achieve a better accuracy in the task of segmentingLiDAR scans. KPRNet improves the convolutional neural network architecture of2D projection methods and utilizes KPConv to replace the commonly usedpost-processing techniques with a learnable point-wise component which allowsus to obtain more accurate 3D labels. With these improvements our modeloutperforms the current best method on the SemanticKITTI benchmark, reaching anmIoU of 63.1.

KPRNet: Improving projection-based LiDAR semantic segmentation